The Application of SIFT Method towards Image Registration

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Abstract:

The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved greatly, which is applicable for registering remote sensing images of large areas.

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Advanced Materials Research (Volumes 1044-1045)

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1392-1396

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October 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] David G. Lowe. Local Feature View Clustering for 3D Object Recognition[C]. Proc. of IEEE Conference on Computer Vision and Patten Recognition, Kauai, Hawaii, December (2001).

DOI: 10.1109/cvpr.2001.990541

Google Scholar

[2] Li Q L, Wang G Y, Liu J G and Chen S B. 2009. Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2): 287–29.

DOI: 10.1109/lgrs.2008.2011751

Google Scholar

[3] David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints [J]. The International Journal of Computer Vision, (2004).

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[4] Li F F, Xiao B L, Jia Y H and Mao X L. 2009. Improved SIFT algorithm and its application in automatic registration of remotelysensed imagery. Geomatics and Information Science of Wuhan University, 34(10): 1245–1249.

Google Scholar

[4] Dong L X. 2008. Estimation of the Forest Canopy Height and Above ground Biomass Based on Multiplicatel Remote Sensing in Three Gorges. Beijing: Institute of Remote Sensing Applications Chinese Academy of Sciences (CAS): 56–98.

Google Scholar

[5] A. Smola.Support Vector learning: Concepts and Algorithms.Tutorial given at ISCAS, [R] Sydney, 2001.

Google Scholar

[6] B. Scholkopf and A.J. Smola.Support Vector Machines and Kernel Algorithms[R].In Encyclopedia of Biostatistics, John Wiley and Sons, (2003).

Google Scholar